---
title: SQLServer
---

>Azure SQL provides a dedicated [Vector data type](https:\learn.microsoft.com\sql\t-sql\data-types\vector-data-type?view=azuresqldb-current&viewFallbackFrom=sql-server-ver16&tabs=csharp-sample) that simplifies the creation, storage, and querying of vector embeddings directly within a relational database. This eliminates the need for separate vector databases and related integrations, increasing the security of your solutions while reducing the overall complexity.

Azure SQL is a robust service that combines scalability, security, and high availability, providing all the benefits of a modern database solution. It leverages a sophisticated query optimizer and enterprise features to perform vector similarity searches alongside traditional SQL queries, enhancing data analysis and decision-making.

Read more on using [Intelligent applications with Azure SQL Database](https://learn.microsoft.com/azure/azure-sql/database/ai-artificial-intelligence-intelligent-applications?view=azuresql)

This notebook shows you how to leverage this integrated SQL [vector database](https://devblogs.microsoft.com/azure-sql/exciting-announcement-public-preview-of-native-vector-support-in-azure-sql-database/) to store documents and perform vector search queries using Cosine (cosine distance), L2 (Euclidean distance), and IP (inner product) to locate documents close to the query vectors

## Setup

Install the `langchain-sqlserver` python package.

The code lives in an integration package called:[langchain-sqlserver](https:\github.com\langchain-ai\langchain-azure\tree\main\libs\sqlserver).

```python
!pip install langchain-sqlserver==0.1.1
```

## Credentials

There are no credentials needed to run this notebook, just make sure you downloaded the `langchain-sqlserver` package
If you want to get best in-class automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:

```python
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
# os.environ["LANGSMITH_TRACING"] = "true"
```

## Initialization

```python
from langchain_sqlserver import SQLServer_VectorStore
```

Find your Azure SQL DB connection string in the Azure portal under your database settings

For more info: [Connect to Azure SQL DB - Python](https:\learn.microsoft.com\en-us\azure\azure-sql\database\connect-query-python?view=azuresql)

```python
import os

import pyodbc

# Define your SQLServer Connection String
_CONNECTION_STRING = (
    "Driver={ODBC Driver 18 for SQL Server};"
    "Server=<YOUR_DBSERVER>.database.windows.net,1433;"
    "Database=test;"
    "TrustServerCertificate=yes;"
    "Connection Timeout=60;"
    "LongAsMax=yes;"
)

# Connection string can vary:
# "mssql+pyodbc://<username>:<password><servername>/<dbname>?driver=ODBC+Driver+18+for+SQL+Server" -> With Username and Password specified
# "mssql+pyodbc://<servername>/<dbname>?driver=ODBC+Driver+18+for+SQL+Server&Trusted_connection=yes" -> Uses Trusted connection
# "mssql+pyodbc://<servername>/<dbname>?driver=ODBC+Driver+18+for+SQL+Server" -> Uses EntraID connection
# "mssql+pyodbc://<servername>/<dbname>?driver=ODBC+Driver+18+for+SQL+Server&Trusted_connection=no" -> Uses EntraID connection
```

In this example we use Azure OpenAI to generate embeddings , however you can use different embeddings provided in LangChain.

You can deploy a version of Azure OpenAI instance on Azure Portal following this [guide](https:\learn.microsoft.com\en-us\azure\ai-services\openai\how-to\create-resource?pivots=web-portal). Once you have your instance running, make sure you have the name of your instance and key. You can find the key in the Azure Portal, under the "Keys and Endpoint" section of your instance.

```python
!pip install langchain-openai
```

```python
# Import the necessary Libraries
from langchain_openai import AzureChatOpenAI, AzureOpenAIEmbeddings

# Set your AzureOpenAI details
azure_endpoint = "https://<YOUR_ENDPOINT>.openai.azure.com/"
azure_deployment_name_embedding = "text-embedding-3-small"
azure_deployment_name_chatcompletion = "chatcompletion"
azure_api_version = "2023-05-15"
azure_api_key = "YOUR_KEY"


# Use AzureChatOpenAI for chat completions
llm = AzureChatOpenAI(
    azure_endpoint=azure_endpoint,
    azure_deployment=azure_deployment_name_chatcompletion,
    openai_api_version=azure_api_version,
    openai_api_key=azure_api_key,
)

# Use AzureOpenAIEmbeddings for embeddings
embeddings = AzureOpenAIEmbeddings(
    azure_endpoint=azure_endpoint,
    azure_deployment=azure_deployment_name_embedding,
    openai_api_version=azure_api_version,
    openai_api_key=azure_api_key,
)
```

## Manage vector store

```python
from langchain_community.vectorstores.utils import DistanceStrategy
from langchain_sqlserver import SQLServer_VectorStore

# Initialize the vector store
vector_store = SQLServer_VectorStore(
    connection_string=_CONNECTION_STRING,
    distance_strategy=DistanceStrategy.COSINE,  # optional, if not provided, defaults to COSINE
    embedding_function=embeddings,  # you can use different embeddings provided in LangChain
    embedding_length=1536,
    table_name="langchain_test_table",  # using table with a custom name
)
```

### Add items to vector store

```python
## we will use some artificial data for this example
query = [
    "I have bought several of the Vitality canned dog food products and have found them all to be of good quality. The product looks more like a stew than a processed meat and it smells better. My Labrador is finicky and she appreciates this product better than  most.",
    "The candy is just red , No flavor . Just  plan and chewy .  I would never buy them again",
    "Arrived in 6 days and were so stale i could not eat any of the 6 bags!!",
    "Got these on sale for roughly 25 cents per cup, which is half the price of my local grocery stores, plus they rarely stock the spicy flavors. These things are a GREAT snack for my office where time is constantly crunched and sometimes you can't escape for a real meal. This is one of my favorite flavors of Instant Lunch and will be back to buy every time it goes on sale.",
    "If you are looking for a less messy version of licorice for the children, then be sure to try these!  They're soft, easy to chew, and they don't get your hands all sticky and gross in the car, in the summer, at the beach, etc. We love all the flavos and sometimes mix these in with the chocolate to have a very nice snack! Great item, great price too, highly recommend!",
    "We had trouble finding this locally - delivery was fast, no more hunting up and down the flour aisle at our local grocery stores.",
    "Too much of a good thing? We worked this kibble in over time, slowly shifting the percentage of Felidae to national junk-food brand until the bowl was all natural. By this time, the cats couldn't keep it in or down. What a mess. We've moved on.",
    "Hey, the description says 360 grams - that is roughly 13 ounces at under $4.00 per can. No way - that is the approximate price for a 100 gram can.",
    "The taste of these white cheddar flat breads is like a regular cracker - which is not bad, except that I bought them because I wanted a cheese taste.<br /><br />What was a HUGE disappointment? How misleading the packaging of the box is. The photo on the box (I bought these in store) makes it look like it is full of long flatbreads (expanding the length and width of the box). Wrong! The plastic tray that holds the crackers is about 2"
    " smaller all around - leaving you with about 15 or so small flatbreads.<br /><br />What is also bad about this is that the company states they use biodegradable and eco-friendly packaging. FAIL! They used a HUGE box for a ridiculously small amount of crackers. Not ecofriendly at all.<br /><br />Would I buy these again? No - I feel ripped off. The other crackers (like Sesame Tarragon) give you a little<br />more bang for your buck and have more flavor.",
    "I have used this product in smoothies for my son and he loves it. Additionally, I use this oil in the shower as a skin conditioner and it has made my skin look great. Some of the stretch marks on my belly has disappeared quickly. Highly recommend!!!",
    "Been taking Coconut Oil for YEARS.  This is the best on the retail market.  I wish it was in glass, but this is the one.",
]

query_metadata = [
    {"id": 1, "summary": "Good Quality Dog Food"},
    {"id": 8, "summary": "Nasty No flavor"},
    {"id": 4, "summary": "stale product"},
    {"id": 11, "summary": "Great value and convenient ramen"},
    {"id": 5, "summary": "Great for the kids!"},
    {"id": 2, "summary": "yum falafel"},
    {"id": 9, "summary": "Nearly killed the cats"},
    {"id": 6, "summary": "Price cannot be correct"},
    {"id": 3, "summary": "Taste is neutral, quantity is DECEITFUL!"},
    {"id": 7, "summary": "This stuff is great"},
    {"id": 10, "summary": "The reviews don't lie"},
]
```

```python
vector_store.add_texts(texts=query, metadatas=query_metadata)
```

```output
[1, 8, 4, 11, 5, 2, 9, 6, 3, 7, 10]
```

## Query vector store

Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.

Performing a simple similarity search can be done as follows:

```python
# Perform a similarity search between the embedding of the query and the embeddings of the documents
simsearch_result = vector_store.similarity_search("Good reviews", k=3)
print(simsearch_result)
```

```output
[Document(metadata={'id': 1, 'summary': 'Good Quality Dog Food'}, page_content='I have bought several of the Vitality canned dog food products and have found them all to be of good quality. The product looks more like a stew than a processed meat and it smells better. My Labrador is finicky and she appreciates this product better than  most.'), Document(metadata={'id': 7, 'summary': 'This stuff is great'}, page_content='I have used this product in smoothies for my son and he loves it. Additionally, I use this oil in the shower as a skin conditioner and it has made my skin look great. Some of the stretch marks on my belly has disappeared quickly. Highly recommend!!!'), Document(metadata={'id': 5, 'summary': 'Great for the kids!'}, page_content="If you are looking for a less messy version of licorice for the children, then be sure to try these!  They're soft, easy to chew, and they don't get your hands all sticky and gross in the car, in the summer, at the beach, etc. We love all the flavos and sometimes mix these in with the chocolate to have a very nice snack! Great item, great price too, highly recommend!")]
```

### Filtering Support

The vectorstore supports a set of filters that can be applied against the metadata fields of the documents.This feature enables developers and data analysts to refine their queries, ensuring that the search results are accurately aligned with their needs. By applying filters based on specific metadata attributes, users can limit the scope of their searches, concentrating only on the most relevant data subsets.

```python
# hybrid search -> filter for cases where id not equal to 1.
hybrid_simsearch_result = vector_store.similarity_search(
    "Good reviews", k=3, filter={"id": {"$ne": 1}}
)
print(hybrid_simsearch_result)
```

```output
[Document(metadata={'id': 7, 'summary': 'This stuff is great'}, page_content='I have used this product in smoothies for my son and he loves it. Additionally, I use this oil in the shower as a skin conditioner and it has made my skin look great. Some of the stretch marks on my belly has disappeared quickly. Highly recommend!!!'), Document(metadata={'id': 5, 'summary': 'Great for the kids!'}, page_content="If you are looking for a less messy version of licorice for the children, then be sure to try these!  They're soft, easy to chew, and they don't get your hands all sticky and gross in the car, in the summer, at the beach, etc. We love all the flavos and sometimes mix these in with the chocolate to have a very nice snack! Great item, great price too, highly recommend!"), Document(metadata={'id': 3, 'summary': 'Taste is neutral, quantity is DECEITFUL!'}, page_content='The taste of these white cheddar flat breads is like a regular cracker - which is not bad, except that I bought them because I wanted a cheese taste.<br /><br />What was a HUGE disappointment? How misleading the packaging of the box is. The photo on the box (I bought these in store) makes it look like it is full of long flatbreads (expanding the length and width of the box). Wrong! The plastic tray that holds the crackers is about 2 smaller all around - leaving you with about 15 or so small flatbreads.<br /><br />What is also bad about this is that the company states they use biodegradable and eco-friendly packaging. FAIL! They used a HUGE box for a ridiculously small amount of crackers. Not ecofriendly at all.<br /><br />Would I buy these again? No - I feel ripped off. The other crackers (like Sesame Tarragon) give you a little<br />more bang for your buck and have more flavor.')]
```

### Similarity Search with Score

If you want to execute a similarity search and receive the corresponding scores you can run:

```python
simsearch_with_score_result = vector_store.similarity_search_with_score(
    "Not a very good product", k=12
)
print(simsearch_with_score_result)
```

```output
[(Document(metadata={'id': 3, 'summary': 'Taste is neutral, quantity is DECEITFUL!'}, page_content='The taste of these white cheddar flat breads is like a regular cracker - which is not bad, except that I bought them because I wanted a cheese taste.<br /><br />What was a HUGE disappointment? How misleading the packaging of the box is. The photo on the box (I bought these in store) makes it look like it is full of long flatbreads (expanding the length and width of the box). Wrong! The plastic tray that holds the crackers is about 2 smaller all around - leaving you with about 15 or so small flatbreads.<br /><br />What is also bad about this is that the company states they use biodegradable and eco-friendly packaging. FAIL! They used a HUGE box for a ridiculously small amount of crackers. Not ecofriendly at all.<br /><br />Would I buy these again? No - I feel ripped off. The other crackers (like Sesame Tarragon) give you a little<br />more bang for your buck and have more flavor.'), 0.651870006770711), (Document(metadata={'id': 8, 'summary': 'Nasty No flavor'}, page_content='The candy is just red , No flavor . Just  plan and chewy .  I would never buy them again'), 0.6908952973052638), (Document(metadata={'id': 4, 'summary': 'stale product'}, page_content='Arrived in 6 days and were so stale i could not eat any of the 6 bags!!'), 0.7360955776468822), (Document(metadata={'id': 1, 'summary': 'Good Quality Dog Food'}, page_content='I have bought several of the Vitality canned dog food products and have found them all to be of good quality. The product looks more like a stew than a processed meat and it smells better. My Labrador is finicky and she appreciates this product better than  most.'), 0.7408823529514486), (Document(metadata={'id': 9, 'summary': 'Nearly killed the cats'}, page_content="Too much of a good thing? We worked this kibble in over time, slowly shifting the percentage of Felidae to national junk-food brand until the bowl was all natural. By this time, the cats couldn't keep it in or down. What a mess. We've moved on."), 0.782995248991772), (Document(metadata={'id': 7, 'summary': 'This stuff is great'}, page_content='I have used this product in smoothies for my son and he loves it. Additionally, I use this oil in the shower as a skin conditioner and it has made my skin look great. Some of the stretch marks on my belly has disappeared quickly. Highly recommend!!!'), 0.7912681479906212), (Document(metadata={'id': 2, 'summary': 'yum falafel'}, page_content='We had trouble finding this locally - delivery was fast, no more hunting up and down the flour aisle at our local grocery stores.'), 0.809213468778896), (Document(metadata={'id': 10, 'summary': "The reviews don't lie"}, page_content='Been taking Coconut Oil for YEARS.  This is the best on the retail market.  I wish it was in glass, but this is the one.'), 0.8281482301097155), (Document(metadata={'id': 5, 'summary': 'Great for the kids!'}, page_content="If you are looking for a less messy version of licorice for the children, then be sure to try these!  They're soft, easy to chew, and they don't get your hands all sticky and gross in the car, in the summer, at the beach, etc. We love all the flavos and sometimes mix these in with the chocolate to have a very nice snack! Great item, great price too, highly recommend!"), 0.8283754326400574), (Document(metadata={'id': 6, 'summary': 'Price cannot be correct'}, page_content='Hey, the description says 360 grams - that is roughly 13 ounces at under $4.00 per can. No way - that is the approximate price for a 100 gram can.'), 0.8323967822635847), (Document(metadata={'id': 11, 'summary': 'Great value and convenient ramen'}, page_content="Got these on sale for roughly 25 cents per cup, which is half the price of my local grocery stores, plus they rarely stock the spicy flavors. These things are a GREAT snack for my office where time is constantly crunched and sometimes you can't escape for a real meal. This is one of my favorite flavors of Instant Lunch and will be back to buy every time it goes on sale."), 0.8387189489406939)]
```

For a full list of the different searches you can execute on a Azure SQL vector store, please refer to the [API reference](https://python.langchain.com/api_reference/sqlserver/index.html).

### Similarity Search when you already have embeddings you want to search on

```python
# if you already have embeddings you want to search on
simsearch_by_vector = vector_store.similarity_search_by_vector(
    [-0.0033353185281157494, -0.017689190804958344, -0.01590404286980629, ...]
)
print(simsearch_by_vector)
```

```output
[Document(metadata={'id': 8, 'summary': 'Nasty No flavor'}, page_content='The candy is just red , No flavor . Just  plan and chewy .  I would never buy them again'), Document(metadata={'id': 4, 'summary': 'stale product'}, page_content='Arrived in 6 days and were so stale i could not eat any of the 6 bags!!'), Document(metadata={'id': 3, 'summary': 'Taste is neutral, quantity is DECEITFUL!'}, page_content='The taste of these white cheddar flat breads is like a regular cracker - which is not bad, except that I bought them because I wanted a cheese taste.<br /><br />What was a HUGE disappointment? How misleading the packaging of the box is. The photo on the box (I bought these in store) makes it look like it is full of long flatbreads (expanding the length and width of the box). Wrong! The plastic tray that holds the crackers is about 2 smaller all around - leaving you with about 15 or so small flatbreads.<br /><br />What is also bad about this is that the company states they use biodegradable and eco-friendly packaging. FAIL! They used a HUGE box for a ridiculously small amount of crackers. Not ecofriendly at all.<br /><br />Would I buy these again? No - I feel ripped off. The other crackers (like Sesame Tarragon) give you a little<br />more bang for your buck and have more flavor.'), Document(metadata={'id': 6, 'summary': 'Price cannot be correct'}, page_content='Hey, the description says 360 grams - that is roughly 13 ounces at under $4.00 per can. No way - that is the approximate price for a 100 gram can.')]
```

```python
# Similarity Search with Score if you already have embeddings you want to search on
simsearch_by_vector_with_score = vector_store.similarity_search_by_vector_with_score(
    [-0.0033353185281157494, -0.017689190804958344, -0.01590404286980629, ...]
)
print(simsearch_by_vector_with_score)
```

```output
[(Document(metadata={'id': 8, 'summary': 'Nasty No flavor'}, page_content='The candy is just red , No flavor . Just  plan and chewy .  I would never buy them again'), 0.9648153551769503), (Document(metadata={'id': 4, 'summary': 'stale product'}, page_content='Arrived in 6 days and were so stale i could not eat any of the 6 bags!!'), 0.9655108580341948), (Document(metadata={'id': 3, 'summary': 'Taste is neutral, quantity is DECEITFUL!'}, page_content='The taste of these white cheddar flat breads is like a regular cracker - which is not bad, except that I bought them because I wanted a cheese taste.<br /><br />What was a HUGE disappointment? How misleading the packaging of the box is. The photo on the box (I bought these in store) makes it look like it is full of long flatbreads (expanding the length and width of the box). Wrong! The plastic tray that holds the crackers is about 2 smaller all around - leaving you with about 15 or so small flatbreads.<br /><br />What is also bad about this is that the company states they use biodegradable and eco-friendly packaging. FAIL! They used a HUGE box for a ridiculously small amount of crackers. Not ecofriendly at all.<br /><br />Would I buy these again? No - I feel ripped off. The other crackers (like Sesame Tarragon) give you a little<br />more bang for your buck and have more flavor.'), 0.9840511208615808), (Document(metadata={'id': 6, 'summary': 'Price cannot be correct'}, page_content='Hey, the description says 360 grams - that is roughly 13 ounces at under $4.00 per can. No way - that is the approximate price for a 100 gram can.'), 0.9915737524649991)]
```

## Delete items from vector store

### Delete Row by ID

```python
# delete row by id
vector_store.delete(["3", "7"])
```

```output
True
```

### Drop Vector Store

```python
# drop vectorstore
vector_store.drop()
```

## Load a Document from Azure Blob Storage

Below is example of loading a file from Azure Blob Storage container into the SQL Vector store after splitting the document into chunks.
[Azure Blog Storage](https://learn.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction) is Microsoft's object storage solution for the cloud. Blob Storage is optimized for storing massive amounts of unstructured data.

```python
pip install azure-storage-blob
```

```python
from langchain.document_loaders import AzureBlobStorageFileLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_core.documents import Document

# Define your connection string and blob details
conn_str = "DefaultEndpointsProtocol=https;AccountName=<YourBlobName>;AccountKey=<YourAccountKey>==;EndpointSuffix=core.windows.net"
container_name = "<YourContainerName"
blob_name = "01 Harry Potter and the Sorcerers Stone.txt"

# Create an instance of AzureBlobStorageFileLoader
loader = AzureBlobStorageFileLoader(
    conn_str=conn_str, container=container_name, blob_name=blob_name
)

# Load the document from Azure Blob Storage
documents = loader.load()

# Split the document into smaller chunks if necessary
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
split_documents = text_splitter.split_documents(documents)

# Print the number of split documents
print(f"Number of split documents: {len(split_documents)}")
```

```output
Number of split documents: 528
```

API Reference:[AzureBlobStorageContainerLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.azure_blob_storage_container.AzureBlobStorageContainerLoader.html)

```python
# # Initialize the vector store & insert the documents in AzureSQLDB with their embeddings
vector_store = SQLServer_VectorStore(
    connection_string=_CONNECTION_STRING,
    distance_strategy=DistanceStrategy.COSINE,
    embedding_function=embeddings,
    embedding_length=1536,
    table_name="harrypotter",
)  # Replace with your actual vector store initialization

# Add split documents to the vector store individually
for i, doc in enumerate(split_documents):
    vector_store.add_documents(documents=[doc], ids=[f"doc_{i}"])

print("Documents added to the vector store successfully!")
```

```output
Documents added to the vector store successfully!
```

## Query directly

```python
from typing import List, Tuple

# Perform similarity search
query = "Why did the Dursleys not want Harry in their house?"
docs_with_score: List[Tuple[Document, float]] = (
    vector_store.similarity_search_with_score(query)
)

for doc, score in docs_with_score:
    print("-" * 60)
    print("Score: ", score)
    print(doc.page_content)
    print("-" * 60)
```

```output
------------------------------------------------------------
Score:  0.3626232679001803
The Dursleys had everything they wanted, but they also had a secret, and their greatest fear was that somebody would discover it. They didn’t think they could bear it if anyone found out about the Potters. Mrs. Potter was Mrs. Dursley’s sister, but they hadn’t met for several years; in fact, Mrs. Dursley pretended she didn’t have a sister, because her sister and her good-for-nothing husband were as unDursleyish as it was possible to be. The Dursleys shuddered to think what the neighbors would say if the Potters arrived in the street. The Dursleys knew that the Potters had a small son, too, but they had never even seen him. This boy was another good reason for keeping the Potters away; they didn’t want Dudley mixing with a child like that.
------------------------------------------------------------
------------------------------------------------------------
Score:  0.44752797298657554
The Dursleys’ house had four bedrooms: one for Uncle Vernon and Aunt Petunia, one for visitors (usually Uncle Vernon’s sister, Marge), one where Dudley slept, and one where Dudley kept all the toys and things that wouldn’t fit into his first bedroom. It only took Harry one trip upstairs to move everything he owned from the cupboard to this room. He sat down on the bed and stared around him. Nearly everything in here was broken. The month-old video camera was lying on top of a small, working tank Dudley had once driven over the next door neighbor’s dog; in the corner was Dudley’s first-ever television set, which he’d put his foot through when his favorite program had been canceled; there was a large birdcage, which had once held a parrot that Dudley had swapped at school for a real air rifle, which was up on a shelf with the end all bent because Dudley had sat on it. Other shelves were full of books. They were the only things in the room that looked as though they’d never been touched.
------------------------------------------------------------
------------------------------------------------------------
Score:  0.4652486419877385
M r. and Mrs. Dursley, of number four, Privet Drive, were proud to say that they were perfectly normal, thank you very much. They were the last people you’d expect to be involved in anything strange or mysterious, because they just didn’t hold with such nonsense.

Mr. Dursley was the director of a firm called Grunnings, which made drills. He was a big, beefy man with hardly any neck, although he did have a very large mustache. Mrs. Dursley was thin and blonde and had nearly twice the usual amount of neck, which came in very useful as she spent so much of her time craning over garden fences, spying on the neighbors. The Dursleys had a small son called Dudley and in their opinion there was no finer boy anywhere.
------------------------------------------------------------
------------------------------------------------------------
Score:  0.4739086301927252
Hagrid was watching him sadly.

“Took yeh from the ruined house myself, on Dumbledore’s orders. Brought yeh ter this lot….”

“Load of old tosh,” said Uncle Vernon. Harry jumped; he had almost forgotten that the Dursleys were there. Uncle Vernon certainly seemed to have got back his courage. He was glaring at Hagrid and his fists were clenched.

“Now, you listen here, boy,” he snarled, “I accept there’s something strange about you, probably nothing a good beating wouldn’t have cured — and as for all this about your parents, well, they were weirdoes, no denying it, and the world’s better off without them in my opinion — asked for all they got, getting mixed up with these wizarding types — just what I expected, always knew they’d come to a sticky end —”

But at that moment, Hagrid leapt from the sofa and drew a battered pink umbrella from inside his coat. Pointing this at Uncle Vernon like a sword, he said, “I’m warning you, Dursley — I’m warning you — one more word….”
------------------------------------------------------------
```

## Usage for retrieval-augmented generation

#### Use Case 1: Q&A System based on the Story Book

The Q&A function allows users to ask specific questions about the story, characters, and events, and get concise, context-rich answers. This not only enhances their understanding of the books but also makes them feel like they're part of the magical universe.

## Query by turning into retriever

The LangChain Vector store simplifies building sophisticated Q&A systems by enabling efficient similarity searches to find the top 10 relevant documents based on the user's query. The **retriever** is created from the **vector\_store,** and the question-answer chain is built using the **create\_stuff\_documents\_chain** function. A prompt template is crafted using the **ChatPromptTemplate** class, ensuring structured and context-rich responses. Often in Q&A applications it's important to show users the sources that were used to generate the answer. LangChain's built-in **create\_retrieval\_chain** will propagate retrieved source documents to the output under the "context" key:

Read more about LangChain RAG tutorials & the terminologies mentioned above [here](/oss/langchain/rag)

```python
from typing import List, Tuple

import pandas as pd
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate


# Define the function to perform the RAG chain invocation
def get_answer_and_sources(user_query: str):
    # Perform similarity search with scores
    docs_with_score: List[Tuple[Document, float]] = (
        vector_store.similarity_search_with_score(
            user_query,
            k=10,
        )
    )

    # Extract the context from the top results
    context = "\n".join([doc.page_content for doc, score in docs_with_score])

    # Define the system prompt
    system_prompt = (
        "You are an assistant for question-answering tasks based on the story in the book. "
        "Use the following pieces of retrieved context to answer the question. "
        "If you don't know the answer, say that you don't know, but also suggest that the user can use the fan fiction function to generate fun stories. "
        "Use 5 sentences maximum and keep the answer concise by also providing some background context of 1-2 sentences."
        "\n\n"
        "{context}"
    )

    # Create the prompt template
    prompt = ChatPromptTemplate.from_messages(
        [
            ("system", system_prompt),
            ("human", "{input}"),
        ]
    )

    # Create the retriever and chains
    retriever = vector_store.as_retriever()
    question_answer_chain = create_stuff_documents_chain(llm, prompt)
    rag_chain = create_retrieval_chain(retriever, question_answer_chain)

    # Define the input
    input_data = {"input": user_query}

    # Invoke the RAG chain
    response = rag_chain.invoke(input_data)

    # Print the answer
    print("Answer:", response["answer"])

    # Prepare the data for the table
    data = {
        "Doc ID": [
            doc.metadata.get("source", "N/A").split("/")[-1]
            for doc in response["context"]
        ],
        "Content": [
            doc.page_content[:50] + "..."
            if len(doc.page_content) > 100
            else doc.page_content
            for doc in response["context"]
        ],
    }

    # Create a DataFrame
    df = pd.DataFrame(data)

    # Print the table
    print("\nSources:")
    print(df.to_markdown(index=False))
```

```python
# Define the user query
user_query = "How did Harry feel when he first learnt that he was a Wizard?"

# Call the function to get the answer and sources
get_answer_and_sources(user_query)
```

```output
Answer: When Harry first learned that he was a wizard, he felt quite sure there had been a horrible mistake. He struggled to believe it because he had spent his life being bullied and mistreated by the Dursleys. If he was really a wizard, he wondered why he hadn't been able to use magic to defend himself. This disbelief and surprise were evident when he gasped, “I’m a what?”

Sources:
| Doc ID                                      | Content                                               |
|:--------------------------------------------|:------------------------------------------------------|
| 01 Harry Potter and the Sorcerers Stone.txt | Harry was wondering what a wizard did once he’d fi... |
| 01 Harry Potter and the Sorcerers Stone.txt | Harry realized his mouth was open and closed it qu... |
| 01 Harry Potter and the Sorcerers Stone.txt | “Most of us reckon he’s still out there somewhere ... |
| 01 Harry Potter and the Sorcerers Stone.txt | “Ah, go boil yer heads, both of yeh,” said Hagrid.... |
```

```python
# Define the user query
user_query = "Did Harry have a pet? What was it"

# Call the function to get the answer and sources
get_answer_and_sources(user_query)
```

```output
Yes, Harry had a pet owl named Hedwig. He decided to call her Hedwig after finding the name in a book titled *A History of Magic*.

Sources:
| Doc ID                                      | Content                                               |
|:--------------------------------------------|:------------------------------------------------------|
| 01 Harry Potter and the Sorcerers Stone.txt | Harry sank down next to the bowl of peas. “What di... |
| 01 Harry Potter and the Sorcerers Stone.txt | Harry kept to his room, with his new owl for compa... |
| 01 Harry Potter and the Sorcerers Stone.txt | As the snake slid swiftly past him, Harry could ha... |
| 01 Harry Potter and the Sorcerers Stone.txt | Ron reached inside his jacket and pulled out a fat... |
```

## API reference

For detailed documentation of SQLServer Vectorstore features and configurations head to the API reference: [https://python.langchain.com/api\_reference/sqlserver/index.html](https:\python.langchain.com\api_reference\sqlserver\index.html)

## Related

- Vector store [conceptual guide](https://python.langchain.com/docs/concepts/vectorstores/)
- Vector store [how-to guides](https://python.langchain.com/docs/how_to/#vector-stores)
